System

Abstract

An object of a system according to an embodiment is to enable a foreigner visiting Japan to intuitively understand the contents of food from a menu or an image of a restaurant.SOLUTION: A system includes an analysis unit, a processing unit, a translation unit, and a provision unit. The analysis unit analyzes the menu or the image. The processing unit performs processing based on the user's preference and allergy information based on the information analyzed by the analysis unit. The translation unit translates the information processed by the processing unit. The providing unit provides the user with the information translated by the translation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult for foreign visitors to Japan to understand the contents of food from restaurant menus and images, and there is room for improvement.

[0005] The system according to the embodiment aims to enable foreign visitors to Japan to intuitively understand the contents of food from restaurant menus and images. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a processing unit, a translation unit, and a providing unit. The analysis unit analyzes a menu or image. The processing unit processes the user's preferences and allergy information based on the information analyzed by the analysis unit. The translation unit translates the information processed by the processing unit. The providing unit provides the information translated by the translation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can enable foreign visitors to Japan to intuitively understand the contents of food from restaurant menus and images. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A navigation system according to an embodiment of the present invention navigates foreign visitors to Japan by navigating restaurant menus and images, often confusing them with food selection. AI translates and explains the unique etiquette associated with each restaurant in a user-friendly format. The navigation system uses a user to input a restaurant menu or image, and the AI ​​analyzes the menu or image to identify the type of food. The system then translates information about the identified food and the unique etiquette associated with that food, providing it to the user in a user-friendly format. For example, if a user views a menu for a specific dish at a Japanese restaurant, the AI ​​analyzes the menu and identifies the type of food. The AI ​​then translates information about the dish and the associated etiquette and provides it to the user. This allows foreign visitors to Japan to enjoy their meal at a Japanese restaurant more. The AI ​​can also suggest appropriate foods based on the user's preferences and allergy information. For example, if a user inputs allergy information, the AI ​​can suggest foods that address the allergy based on that information. This allows the user to enjoy their meal with peace of mind. Furthermore, the AI ​​also translates the unique etiquette associated with each restaurant and provides it to the user. For example, it can translate etiquette at Japanese restaurants and dining etiquette, and provide it to users in an easy-to-understand format. This allows foreign visitors to Japan to understand Japanese culture and have a better experience. In this way, the navigation system can improve the dining experience of foreign visitors to Japan. For example, foreign visitors to Japan can enjoy their meals at Japanese restaurants more and understand Japanese culture and etiquette. AI can analyze menus and images, and translate and digest the information before providing it, improving the dining experience of foreign visitors to Japan.

[0029] A navigation system according to an embodiment includes an analysis unit, a processing unit, a translation unit, and a providing unit. The analysis unit analyzes a menu or image. For example, the analysis unit analyzes a menu image using image recognition technology and converts it into text data. The analysis unit can also analyze the menu text using text analysis technology to identify the type of food. For example, the analysis unit can read a printed menu using OCR technology and convert it into text data. The processing unit processes the information analyzed by the analysis unit based on the user's preferences and allergy information. For example, the processing unit estimates the user's preferences based on the user's past selection history and selects appropriate foods taking into account the allergy information. The processing unit can also suggest allergy-friendly foods based on the user's allergy information. For example, the processing unit selects allergen-free foods based on the allergy information entered by the user. The translation unit translates the information processed by the processing unit. For example, the translation unit translates information about food into the user's native language using machine translation technology. The translation unit can also translate unique food-related etiquette. For example, the translation unit translates etiquette and dining etiquette in Japanese restaurants and provides it to the user. The provision unit provides the information translated by the translation unit to the user. The provision unit provides the information using, for example, text display or audio guidance. The provision unit can also select the optimal display method depending on the user's device. For example, the provision unit displays information in the optimal format depending on the device, such as a smartphone or tablet. This allows the navigation system according to the embodiment to improve the dining experience of foreign visitors to Japan. For example, foreign visitors to Japan can enjoy dining at Japanese restaurants more and understand Japanese culture and etiquette. AI can analyze menus and images, translate and digest the information, and provide it, thereby improving the dining experience of foreign visitors to Japan.

[0030] The analysis unit can analyze a menu or an image and identify a specific food. For example, the analysis unit can use image recognition technology to analyze a menu image and identify a specific food. For example, the analysis unit can use an image recognition algorithm to identify the type of food from the menu image. The analysis unit can also use text analysis technology to analyze the text of a menu and identify a specific food. For example, the analysis unit can use OCR technology to read a printed menu, convert it into text data, and identify the type of food. This allows the analysis of menus and images to identify the food, thereby providing appropriate information to the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the menu into the generation AI and cause the generation AI to perform a process of identifying the type of food from the image data.

[0031] The processing unit can perform processing based on the user's preferences and allergy information. For example, the processing unit estimates the user's preferences based on the user's past selection history and selects appropriate foods taking the allergy information into consideration. For example, the processing unit prioritizes selecting similar dishes based on the user's past selection trends. The processing unit can also suggest foods that accommodate allergies based on the user's allergy information. For example, the processing unit selects foods that do not contain allergens based on the allergy information entered by the user. The processing unit can also customize food suggestions based on the user's preferences and allergy information. For example, the processing unit suggests optimal foods taking the user's preferences and allergy information into consideration. This allows appropriate foods to be suggested by taking the user's preferences and allergy information into consideration. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input the user's preferences and allergy information into the generation AI and cause the generation AI to suggest optimal foods.

[0032] The translation unit can translate information about the identified food and unique manners associated with that food. The translation unit, for example, uses machine translation technology to translate information about the identified food into the user's native language. For example, the translation unit translates nutritional information and ingredient information about the food and provides it to the user. The translation unit can also translate unique manners associated with food. For example, the translation unit translates manners at Japanese restaurants and etiquette while eating and provides it to the user. By translating information about food and manners, the information and manners can be provided to the user in an easy-to-understand format. Some or all of the above-described processing by the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input information about the identified food into a generation AI and have the generation AI perform the translation.

[0033] The providing unit can present the translated information to the user. The providing unit can provide the information using, for example, a text display or voice guidance. For example, the providing unit can display the translated information in text format and provide it to the user. The providing unit can also provide the translated information to the user using voice guidance. For example, the providing unit can provide the translated information by voice using speech synthesis technology. The providing unit can also select the optimal display method depending on the user's device. For example, the providing unit can display the information in an optimal format depending on the device, such as a smartphone or tablet. This makes it easier for the user to understand the translated information provided to the user. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the translated information to a generation AI and have the generation AI execute the optimal display method.

[0034] The consideration unit can suggest foods that accommodate allergies based on the user's allergy information. The consideration unit, for example, selects foods that do not contain allergens based on the allergy information input by the user. For example, the consideration unit prioritizes suggesting dishes that do not contain specific ingredients to which the user is allergic. The consideration unit can also suggest alternative ingredients that accommodate allergies. For example, the consideration unit suggests ingredients that can be used in place of ingredients to which the user is allergic. This allows the user to enjoy their meal with peace of mind by suggesting foods that accommodate allergies. Some or all of the above-mentioned processing by the consideration unit may be performed using, or without, AI, for example. For example, the consideration unit can input the user's allergy information into the generation AI and cause the generation AI to suggest foods that accommodate allergies.

[0035] The translation unit can translate etiquette at Japanese restaurants and etiquette while eating. The translation unit, for example, translates etiquette at Japanese restaurants and etiquette while eating and provides it to the user. For example, the translation unit translates information about etiquette at Japanese restaurants and provides it to the user. The translation unit can also translate information about etiquette while eating and provide it to the user. For example, the translation unit translates information about etiquette while eating into the user's native language and provides it. This makes it easier for the user to understand Japanese culture by translating the etiquette and etiquette at Japanese restaurants. Some or all of the above-mentioned processing by the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input information about etiquette at Japanese restaurants into a generation AI and have the generation AI perform the translation.

[0036] When analyzing menus and images, the analysis unit can improve the accuracy of the analysis by referring to the user's past selection history. For example, the analysis unit prioritizes analysis of similar dishes based on the user's past selection trends. For example, the analysis unit prioritizes analysis of dishes containing specific ingredients based on the user's past selection history. The analysis unit can also provide analysis results that exclude dishes that the user has avoided in the past. For example, the analysis unit provides analysis results that exclude avoided dishes based on the user's past selection history. In this way, by referring to the user's past selection history, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past selection history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0037] The analysis unit can take into account the regional characteristics and seasonality of the dishes when analyzing menus and images. The analysis unit, for example, takes into account seasonal specialties and reflects them in the analysis results. For example, the analysis unit reflects the analysis results based on seasonal specialties. The analysis unit can also prioritize analysis of regional dishes and provide background information. For example, the analysis unit reflects the analysis results based on regional dishes. Furthermore, the analysis unit can prioritize seasonal menus during analysis and suggest them to the user. For example, the analysis unit reflects the analysis results based on seasonal menus. This allows for more appropriate analysis results to be provided by taking into account the regional characteristics and seasonality of the dishes. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the regional characteristics and seasonality of the dishes into the generation AI and have the generation AI perform the analysis.

[0038] When analyzing menus or images, the analysis unit can provide analysis results that include nutritional information about the dishes. The analysis unit, for example, provides analysis results that include calorie information about the dishes. For example, the analysis unit reflects the analysis results based on the calorie information about the dishes. The analysis unit can also include information about major nutrients (protein, fat, carbohydrates, etc.) in the analysis results. For example, the analysis unit reflects the analysis results based on information about the major nutrients. The analysis unit can also analyze the presence or absence of allergens and provide the analysis results to the user. For example, the analysis unit reflects the presence or absence of allergens in the analysis results. In this way, health information can also be provided to the user by including nutritional information about the dishes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input nutritional information about the dishes to the generation AI and have the generation AI provide the analysis results.

[0039] The analysis unit can take the user's geographical location information into consideration when analyzing menus and images. For example, the analysis unit prioritizes analysis of local specialties and famous dishes from the user's current location. For example, the analysis unit analyzes menus of nearby restaurants based on the user's location information. The analysis unit can also analyze dishes containing ingredients unique to the region based on the user's location information. For example, the analysis unit analyzes dishes containing ingredients unique to the region based on the user's location information. This makes it possible to provide information unique to the region by taking the user's geographical location information into consideration. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and have the generation AI perform the analysis.

[0040] The analysis unit can analyze the user's social media activity when analyzing menus and images and provide related food information. The analysis unit, for example, analyzes the menus of restaurants where the user has checked in on social media. For example, the analysis unit analyzes the content of the user's social media posts and provides related food information. The analysis unit can also provide related food information by referring to the activities of the user's friends on social media. For example, the analysis unit provides related food information based on the activities of the user's friends on social media. In this way, related food information can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related food information.

[0041] When analyzing menus or images, the analysis unit can customize the analysis method by reflecting past user feedback. The analysis unit adjusts the analysis method based on, for example, feedback provided by the user in the past. For example, the analysis unit preferentially displays specific analysis results based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis results based on the user's feedback. For example, the analysis unit improves the accuracy of the analysis results based on the user's feedback. In this way, the analysis method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0042] The consideration unit can analyze the user's past meal history and select the optimal consideration method. For example, the consideration unit prioritizes consideration of similar dishes based on the user's past meal selection trends. For example, the consideration unit prioritizes consideration of dishes containing specific ingredients from the user's past meal history. The consideration unit can also provide consideration results by excluding dishes that the user has avoided in the past. For example, the consideration unit provides consideration results by excluding avoided dishes based on the user's past meal history. In this way, the optimal consideration method can be selected by analyzing the user's past meal history. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's past meal history data into the generation AI and cause the generation AI to select the optimal consideration method.

[0043] The consideration unit can take into account the user's current health condition and lifestyle habits. For example, when the user inputs the results of a health checkup, the consideration unit suggests optimal dishes based on the information. For example, the consideration unit suggests health-conscious dishes based on the user's health checkup results. The consideration unit can also suggest appropriate dishes by taking into account the user's lifestyle habits (such as exercise amount and sleep time). For example, the consideration unit can suggest optimal dishes based on the user's lifestyle habit data. Furthermore, the consideration unit can also suggest optimal dishes by taking into account the user's current physical condition (such as fatigue level and stress level). For example, the consideration unit can suggest optimal dishes based on the user's physical condition data. This enables more appropriate suggestions to be made by taking into account the user's health condition and lifestyle habits. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's health condition and lifestyle habit data into the generation AI and cause the generation AI to perform the consideration.

[0044] The consideration unit can make suggestions taking into account the user's dietary restrictions and religious restrictions. For example, if the user is a vegetarian, the consideration unit preferentially suggests dishes that do not contain meat. For example, the consideration unit suggests meat-free dishes based on the fact that the user is a vegetarian. Furthermore, if the user has specific religious restrictions (such as halal or kosher), the consideration unit can also suggest dishes that comply with those restrictions. For example, the consideration unit suggests corresponding dishes based on the user's religious restrictions. Furthermore, if the user has specific dietary restrictions (such as low carbohydrate or low fat), the consideration unit can also suggest dishes that comply with those restrictions. For example, the consideration unit suggests corresponding dishes based on the user's dietary restrictions. This enables appropriate suggestions to be made by taking the user's dietary restrictions and religious restrictions into consideration. Some or all of the above-described processing by the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's dietary restriction and religious restriction data into the generation AI and have the generation AI execute the suggestions.

[0045] The consideration unit can suggest regional ingredients and dishes by taking into account the user's geographical location information. The consideration unit, for example, prioritizes suggestions of local specialties and famous dishes of the region where the user is currently located. For example, the consideration unit suggests menus of nearby restaurants based on the user's location information. The consideration unit can also suggest dishes containing regional ingredients based on the user's location information. For example, the consideration unit suggests dishes containing regional ingredients based on the user's location information. In this way, regional ingredients and dishes can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing by the consideration unit may be performed using AI, for example, or may be performed without using AI. For example, the consideration unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the suggestions.

[0046] The consideration unit can analyze the user's social media activity and take into account related preferences and allergy information. The consideration unit, for example, considers the menu of a restaurant where the user has checked in on social media. For example, the consideration unit analyzes the content of the user's social media posts and takes into account related preferences and allergy information. The consideration unit can also take into account related preferences and allergy information with reference to the activities of the user's friends on social media. For example, the consideration unit takes into account related preferences and allergy information based on the activities of the user's friends on social media. In this way, related preferences and allergy information can be taken into account by analyzing the user's social media activity. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's social media activity data into the generation AI and cause the generation AI to perform the consideration.

[0047] The consideration unit can customize the consideration method by reflecting the user's past feedback. The consideration unit adjusts the consideration method, for example, based on feedback provided by the user in the past. For example, the consideration unit preferentially displays specific consideration results from the user's past feedback. The consideration unit can also improve the accuracy of the consideration results based on the user's feedback. For example, the consideration unit improves the accuracy of the consideration results based on the user's feedback. In this way, the consideration method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the consideration unit may be performed using AI, for example, or may be performed without using AI. For example, the consideration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the consideration method.

[0048] The translation unit can provide information including the cultural background and history of a dish during translation. For example, the translation unit can provide the origin and historical background of a dish by including it in the translation results. For example, the translation unit can translate information about the origin and historical background of a dish and provide it to the user. The translation unit can also provide the cultural meaning and symbolism of a dish by including it in the translation results. For example, the translation unit can translate information about the cultural meaning and symbolism of a dish and provide it to the user. Furthermore, the translation unit can provide the translation results by including information about how a dish developed in a particular region or era. For example, the translation unit can translate information about the development of a dish and provide it to the user. This allows the user to have a deeper understanding by including the cultural background and history of the dish. Some or all of the above-described processing in the translation unit can be performed using, or without, AI. For example, the translation unit can input information about the cultural background and history of a dish into a generation AI and have the generation AI perform the translation.

[0049] During translation, the translation unit can provide information including details of the cooking method and ingredients of the dish. For example, the translation unit translates the cooking steps of the dish in detail and provides it to the user. For example, the translation unit translates information regarding the cooking steps of the dish and provides it to the user. The translation unit can also provide the main ingredients used in the dish and their roles by including them in the translation result. For example, the translation unit translates information regarding the ingredients of the dish and their roles and provides it to the user. Furthermore, the translation unit can also provide tips and precautions regarding the cooking method of the dish by including them in the translation result. For example, the translation unit translates tips and precautions regarding the cooking method of the dish and provides it to the user. In this way, by including details of the cooking method and ingredients of the dish, more specific information can be provided to the user. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input information regarding the cooking method and details of the ingredients of the dish into the generation AI and have the generation AI perform the translation.

[0050] During translation, the translation unit can improve the accuracy of the translation by referring to the user's past translation history. For example, the translation unit may preferentially use similar expressions based on content that the user has translated in the past. For example, the translation unit may preferentially use specific terms and expressions from the user's past translation history. The translation unit can also analyze the user's past translation history and suggest the most appropriate expression. For example, the translation unit may suggest the optimal expression based on the user's past translation history. In this way, by referring to the user's past translation history, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit may input the user's past translation history data into the generation AI and have the generation AI improve the accuracy of the translation.

[0051] During translation, the translation unit can determine the priority of translation based on the time when the dishes were submitted. For example, the translation unit prioritizes translation of seasonal menus or dishes available for a limited time. For example, the translation unit determines the priority of translation based on seasonal menus or dishes available for a limited time. Furthermore, if a user is participating in a specific event or festival, the translation unit can prioritize translation of dishes related to the event. For example, the translation unit determines the priority of translation based on dishes related to the event or festival. Furthermore, the translation unit can prioritize translation of menus of restaurants that the user visits during a specific time period. For example, the translation unit determines the priority of translation based on menus served during a specific time period. In this way, determining the priority of translation based on the time when the dishes were submitted enables more appropriate information to be provided. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input data regarding the time when the dishes were submitted to the generation AI and have the generation AI determine the priority of translation.

[0052] During translation, the translation unit can adjust the order of translation based on the relevance of dishes. For example, the translation unit prioritizes translating the main dish, followed by translating the side dishes and desserts. For example, the translation unit adjusts the order of translation based on the main dish. Furthermore, if a user selects a specific dish, the translation unit can prioritize translating other dishes related to that dish. For example, the translation unit adjusts the order of translation based on other dishes related to the specific dish. Furthermore, if a user is interested in a specific ingredient, the translation unit can prioritize translating dishes containing that ingredient. For example, the translation unit adjusts the order of translation based on dishes containing the specific ingredient. In this way, adjusting the order of translation based on the relevance of dishes enables more appropriate information to be provided. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input data regarding the relevance of dishes to a generation AI and have the generation AI adjust the order of translation.

[0053] During translation, the translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise. For example, if the user is a cooking expert, the translation unit provides a translation that uses a lot of technical terminology. For example, the translation unit provides a translation that uses a lot of technical terminology for cooking experts. Furthermore, if the user is not familiar with cooking, the translation unit can also provide a simple, easy-to-understand translation. For example, the translation unit provides a simple, easy-to-understand translation for users who are not familiar with cooking. Furthermore, the translation unit can adjust the use of appropriate technical terminology based on the user's past translation history. For example, the translation unit adjusts the use of appropriate technical terminology based on the user's past translation history. This allows for a more appropriate translation to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data regarding the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0054] When providing information, the providing unit can select the optimal providing method by referring to the user's past operation history. The providing unit selects the optimal providing method, for example, based on a display method previously selected by the user. For example, the providing unit prioritizes providing specific information based on the user's past operation history. The providing unit can also analyze the user's past operation history and suggest the most appropriate providing method. For example, the providing unit suggests the optimal providing method based on the user's past operation history. In this way, the optimal providing method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal providing method.

[0055] The providing unit can customize the provided content according to the user's current task when providing information. For example, if the user is eating, the providing unit can prioritize providing information related to meals. For example, the providing unit can provide information related to meals based on the fact that the user is eating. Furthermore, if the user is sightseeing, the providing unit can prioritize providing information related to tourist spots. For example, the providing unit can provide information related to tourist spots based on the fact that the user is sightseeing. Furthermore, if the user is shopping, the providing unit can prioritize providing information related to shopping. For example, the providing unit can provide information related to shopping based on the fact that the user is shopping. This enables more appropriate information to be provided by customizing the provided content according to the user's current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data related to the user's current task to a generation AI and cause the generation AI to customize the provided content.

[0056] The providing unit can improve the providing method by reflecting user feedback when providing information. For example, if a user provides feedback on the provided information, the providing unit improves the providing method based on that feedback. For example, the providing unit preferentially provides specific information based on the user feedback. The providing unit can also improve the accuracy of the providing method based on the user feedback. For example, the providing unit improves the accuracy of the providing method based on the user feedback. In this way, the providing method can be improved by reflecting the user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0057] When providing information, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. For example, the providing unit provides a display method optimized for the smartwatch screen size. This makes it possible to select the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0058] When providing information, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the display content based on the language setting of the user's device. For example, the providing unit automatically sets the display content based on the language setting of the device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function based on the user's use of multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide information in that language. For example, the providing unit provides information based on the language selected by the user. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to perform multilingual support for the display content.

[0059] When providing information, the providing unit can analyze the user's social media activity and provide related information. The providing unit, for example, provides information about places where the user has checked in on social media. For example, the providing unit can provide related information based on the user's check-in information. The providing unit can also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide related information based on the user's posts. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit can provide related information based on the activities of the user's friends. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The navigation system also collects real-time feedback from the user while they are eating, and the analysis unit can adjust the analysis results based on that feedback. For example, if a user provides feedback such as "delicious" or "spicy" after eating a dish, the analysis unit can adjust the next analysis results based on that feedback. Also, if a user provides positive feedback about a particular dish, the analysis unit can prioritize the analysis of other dishes related to that dish. Furthermore, if a user provides negative feedback, the analysis unit can also exclude information related to that dish. This allows the system to provide more personalized analysis results by reflecting the user's real-time feedback.

[0062] The navigation system can also monitor the user's health condition, and the analysis unit can adjust the analysis results based on that information. For example, if the user inputs the results of a health check, the analysis unit can prioritize analysis of healthy dishes based on that information. Also, if the user sets a specific health goal (e.g., weight loss or muscle building), the analysis unit can prioritize analysis of dishes that match that goal. Furthermore, if the user has a specific health condition (e.g., high blood pressure or diabetes), the analysis unit can also prioritize analysis of dishes that correspond to that condition. This allows the system to provide more appropriate analysis results by taking the user's health condition into consideration.

[0063] The navigation system can further analyze the user's social media activity and use that information in the analysis unit to adjust the analysis results. For example, the system can analyze the menus of restaurants that the user has checked in to on social media. It can also analyze the content of the user's social media posts to provide related food information. It can also provide related food information by taking into account the activities of the user's friends on social media. This allows the system to provide more appropriate analysis results by analyzing the user's social media activity.

[0064] The navigation system can also improve the information provision method in the provision unit by reflecting the user's past feedback. For example, if the user provides feedback on the provided information, the provision method can be improved based on that feedback. It can also provide specific information preferentially based on the user's feedback. Furthermore, the accuracy of the provision method can be improved based on the user's feedback. In this way, the provision method can be improved by reflecting the user's feedback.

[0065] The navigation system can further adjust the analysis results of the analysis unit by taking into account the user's geographical location information. For example, it can prioritize analysis of local specialties and famous dishes from the area where the user is currently located. It can also analyze menus of nearby restaurants based on the user's location information. It can also analyze dishes that contain ingredients unique to the area based on the user's location information. In this way, it can provide information specific to the area by taking into account the user's geographical location information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The analyzer analyzes the menu or image. For example, the analyzer may use image recognition technology to analyze the image of the menu and convert it into text data. The analyzer may also use text analysis technology to analyze the text of the menu and identify the type of food. For example, the analyzer may use OCR technology to read a printed menu and convert it into text data. Step 2: The processing unit processes the user's preferences and allergy information based on the information analyzed by the analysis unit. For example, the processing unit estimates the user's preferences based on the user's past selection history and selects appropriate foods taking into account the allergy information. The processing unit can also suggest foods that are suitable for allergies based on the user's allergy information. For example, the processing unit selects foods that do not contain allergens based on the allergy information entered by the user. Step 3: The translation unit translates the information processed by the processing unit. For example, the translation unit uses machine translation technology to translate information about food into the user's native language. The translation unit can also translate unique manners related to food. For example, the translation unit translates the manners and etiquette for eating at Japanese restaurants and provides them to the user. Step 4: The providing unit provides the information translated by the translation unit to the user. The providing unit provides the information using, for example, a text display or audio guidance. The providing unit can also select the optimal display method depending on the user's device. For example, the providing unit displays the information in the optimal format depending on the device, such as a smartphone or tablet.

[0068] (Example 2) A navigation system according to an embodiment of the present invention navigates foreign visitors to Japan by navigating restaurant menus and images, often confusing them with food selection. AI translates and explains the unique etiquette associated with each restaurant in a user-friendly format. The navigation system uses a user to input a restaurant menu or image, and the AI ​​analyzes the menu or image to identify the type of food. The system then translates information about the identified food and the unique etiquette associated with that food, providing it to the user in a user-friendly format. For example, if a user views a menu for a specific dish at a Japanese restaurant, the AI ​​analyzes the menu and identifies the type of food. The AI ​​then translates information about the dish and the associated etiquette and provides it to the user. This allows foreign visitors to Japan to enjoy their meal at a Japanese restaurant more. The AI ​​can also suggest appropriate foods based on the user's preferences and allergy information. For example, if a user inputs allergy information, the AI ​​can suggest foods that address the allergy based on that information. This allows the user to enjoy their meal with peace of mind. Furthermore, the AI ​​also translates the unique etiquette associated with each restaurant and provides it to the user. For example, it can translate etiquette at Japanese restaurants and dining etiquette, and provide it to users in an easy-to-understand format. This allows foreign visitors to Japan to understand Japanese culture and have a better experience. In this way, the navigation system can improve the dining experience of foreign visitors to Japan. For example, foreign visitors to Japan can enjoy their meals at Japanese restaurants more and understand Japanese culture and etiquette. AI can analyze menus and images, and translate and digest the information before providing it, improving the dining experience of foreign visitors to Japan.

[0069] A navigation system according to an embodiment includes an analysis unit, a processing unit, a translation unit, and a providing unit. The analysis unit analyzes a menu or image. For example, the analysis unit analyzes a menu image using image recognition technology and converts it into text data. The analysis unit can also analyze the menu text using text analysis technology to identify the type of food. For example, the analysis unit can read a printed menu using OCR technology and convert it into text data. The processing unit processes the information analyzed by the analysis unit based on the user's preferences and allergy information. For example, the processing unit estimates the user's preferences based on the user's past selection history and selects appropriate foods taking into account the allergy information. The processing unit can also suggest allergy-friendly foods based on the user's allergy information. For example, the processing unit selects allergen-free foods based on the allergy information entered by the user. The translation unit translates the information processed by the processing unit. For example, the translation unit translates information about food into the user's native language using machine translation technology. The translation unit can also translate unique food-related etiquette. For example, the translation unit translates etiquette and dining etiquette in Japanese restaurants and provides it to the user. The provision unit provides the information translated by the translation unit to the user. The provision unit provides the information using, for example, text display or audio guidance. The provision unit can also select the optimal display method depending on the user's device. For example, the provision unit displays information in the optimal format depending on the device, such as a smartphone or tablet. This allows the navigation system according to the embodiment to improve the dining experience of foreign visitors to Japan. For example, foreign visitors to Japan can enjoy dining at Japanese restaurants more and understand Japanese culture and etiquette. AI can analyze menus and images, translate and digest the information, and provide it, thereby improving the dining experience of foreign visitors to Japan.

[0070] The analysis unit can analyze a menu or an image and identify a specific food. For example, the analysis unit can use image recognition technology to analyze a menu image and identify a specific food. For example, the analysis unit can use an image recognition algorithm to identify the type of food from the menu image. The analysis unit can also use text analysis technology to analyze the text of a menu and identify a specific food. For example, the analysis unit can use OCR technology to read a printed menu, convert it into text data, and identify the type of food. This allows the analysis of menus and images to identify the food, thereby providing appropriate information to the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the menu into the generation AI and cause the generation AI to perform a process of identifying the type of food from the image data.

[0071] The processing unit can perform processing based on the user's preferences and allergy information. For example, the processing unit estimates the user's preferences based on the user's past selection history and selects appropriate foods taking the allergy information into consideration. For example, the processing unit prioritizes selecting similar dishes based on the user's past selection trends. The processing unit can also suggest foods that accommodate allergies based on the user's allergy information. For example, the processing unit selects foods that do not contain allergens based on the allergy information entered by the user. The processing unit can also customize food suggestions based on the user's preferences and allergy information. For example, the processing unit suggests optimal foods taking the user's preferences and allergy information into consideration. This allows appropriate foods to be suggested by taking the user's preferences and allergy information into consideration. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input the user's preferences and allergy information into the generation AI and cause the generation AI to suggest optimal foods.

[0072] The translation unit can translate information about the identified food and unique manners associated with that food. The translation unit, for example, uses machine translation technology to translate information about the identified food into the user's native language. For example, the translation unit translates nutritional information and ingredient information about the food and provides it to the user. The translation unit can also translate unique manners associated with food. For example, the translation unit translates manners at Japanese restaurants and etiquette while eating and provides it to the user. By translating information about food and manners, the information and manners can be provided to the user in an easy-to-understand format. Some or all of the above-described processing by the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input information about the identified food into a generation AI and have the generation AI perform the translation.

[0073] The providing unit can present the translated information to the user. The providing unit can provide the information using, for example, a text display or voice guidance. For example, the providing unit can display the translated information in text format and provide it to the user. The providing unit can also provide the translated information to the user using voice guidance. For example, the providing unit can provide the translated information by voice using speech synthesis technology. The providing unit can also select the optimal display method depending on the user's device. For example, the providing unit can display the information in an optimal format depending on the device, such as a smartphone or tablet. This makes it easier for the user to understand the translated information provided to the user. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the translated information to a generation AI and have the generation AI execute the optimal display method.

[0074] The consideration unit can suggest foods that accommodate allergies based on the user's allergy information. The consideration unit, for example, selects foods that do not contain allergens based on the allergy information input by the user. For example, the consideration unit prioritizes suggesting dishes that do not contain specific ingredients to which the user is allergic. The consideration unit can also suggest alternative ingredients that accommodate allergies. For example, the consideration unit suggests ingredients that can be used in place of ingredients to which the user is allergic. This allows the user to enjoy their meal with peace of mind by suggesting foods that accommodate allergies. Some or all of the above-mentioned processing by the consideration unit may be performed using, or without, AI, for example. For example, the consideration unit can input the user's allergy information into the generation AI and cause the generation AI to suggest foods that accommodate allergies.

[0075] The translation unit can translate etiquette at Japanese restaurants and etiquette while eating. The translation unit, for example, translates etiquette at Japanese restaurants and etiquette while eating and provides it to the user. For example, the translation unit translates information about etiquette at Japanese restaurants and provides it to the user. The translation unit can also translate information about etiquette while eating and provide it to the user. For example, the translation unit translates information about etiquette while eating into the user's native language and provides it. This makes it easier for the user to understand Japanese culture by translating the etiquette and etiquette at Japanese restaurants. Some or all of the above-mentioned processing by the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input information about etiquette at Japanese restaurants into a generation AI and have the generation AI perform the translation.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis method of menus and images based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the user's facial expression data captured by a camera to estimate emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate emotions. Furthermore, the analysis unit can adjust the analysis method of menus and images based on the estimated user emotions. For example, if the user is nervous, a simple analysis result can be provided and detailed information can be displayed later. Alternatively, if the user is relaxed, a detailed analysis result can be provided, including background information about the dishes. Furthermore, if the user is in a hurry, the most important information can be displayed first, and the analysis result can be quickly provided. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the analysis method based on the emotion.

[0077] When analyzing menus and images, the analysis unit can improve the accuracy of the analysis by referring to the user's past selection history. For example, the analysis unit prioritizes analysis of similar dishes based on the user's past selection trends. For example, the analysis unit prioritizes analysis of dishes containing specific ingredients based on the user's past selection history. The analysis unit can also provide analysis results that exclude dishes that the user has avoided in the past. For example, the analysis unit provides analysis results that exclude avoided dishes based on the user's past selection history. In this way, by referring to the user's past selection history, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past selection history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0078] The analysis unit can take into account the regional characteristics and seasonality of the dishes when analyzing menus and images. The analysis unit, for example, takes into account seasonal specialties and reflects them in the analysis results. For example, the analysis unit reflects the analysis results based on seasonal specialties. The analysis unit can also prioritize analysis of regional dishes and provide background information. For example, the analysis unit reflects the analysis results based on regional dishes. Furthermore, the analysis unit can prioritize seasonal menus during analysis and suggest them to the user. For example, the analysis unit reflects the analysis results based on seasonal menus. This allows for more appropriate analysis results to be provided by taking into account the regional characteristics and seasonality of the dishes. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the regional characteristics and seasonality of the dishes into the generation AI and have the generation AI perform the analysis.

[0079] When analyzing menus or images, the analysis unit can provide analysis results that include nutritional information about the dishes. The analysis unit, for example, provides analysis results that include calorie information about the dishes. For example, the analysis unit reflects the analysis results based on the calorie information about the dishes. The analysis unit can also include information about major nutrients (protein, fat, carbohydrates, etc.) in the analysis results. For example, the analysis unit reflects the analysis results based on information about the major nutrients. The analysis unit can also analyze the presence or absence of allergens and provide the analysis results to the user. For example, the analysis unit reflects the presence or absence of allergens in the analysis results. In this way, health information can also be provided to the user by including nutritional information about the dishes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input nutritional information about the dishes to the generation AI and have the generation AI provide the analysis results.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the user's facial expression data captured by a camera to estimate emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate emotions. The analysis unit can also adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0081] The analysis unit can take the user's geographical location information into consideration when analyzing menus and images. For example, the analysis unit prioritizes analysis of local specialties and famous dishes from the user's current location. For example, the analysis unit analyzes menus of nearby restaurants based on the user's location information. The analysis unit can also analyze dishes containing ingredients unique to the region based on the user's location information. For example, the analysis unit analyzes dishes containing ingredients unique to the region based on the user's location information. This makes it possible to provide information unique to the region by taking the user's geographical location information into consideration. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and have the generation AI perform the analysis.

[0082] The analysis unit can analyze the user's social media activity when analyzing menus and images and provide related food information. The analysis unit, for example, analyzes the menus of restaurants where the user has checked in on social media. For example, the analysis unit analyzes the content of the user's social media posts and provides related food information. The analysis unit can also provide related food information by referring to the activities of the user's friends on social media. For example, the analysis unit provides related food information based on the activities of the user's friends on social media. In this way, related food information can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related food information.

[0083] When analyzing menus or images, the analysis unit can customize the analysis method by reflecting past user feedback. The analysis unit adjusts the analysis method based on, for example, feedback provided by the user in the past. For example, the analysis unit preferentially displays specific analysis results based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis results based on the user's feedback. For example, the analysis unit improves the accuracy of the analysis results based on the user's feedback. In this way, the analysis method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0084] The consideration unit can estimate the user's emotions and adjust the consideration method for preference and allergy information based on the estimated user emotions. The consideration unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the consideration unit can analyze the user's facial expression data captured by a camera to estimate the emotions. The consideration unit can also estimate the user's emotions using voice analysis technology. For example, the consideration unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the consideration unit can adjust the consideration method for preference and allergy information based on the estimated user emotions. For example, if the user is nervous, simple options can be provided and detailed information can be displayed later. Alternatively, if the user is relaxed, detailed options can be provided and customizable information can be suggested. Furthermore, if the user is in a hurry, the most important information can be displayed first and the consideration results can be quickly provided. This allows for more appropriate suggestions by adjusting the consideration method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit may input user emotion data to the generation AI and cause the generation AI to adjust the consideration method based on the emotion.

[0085] The consideration unit can analyze the user's past meal history and select the optimal consideration method. For example, the consideration unit prioritizes consideration of similar dishes based on the user's past meal selection trends. For example, the consideration unit prioritizes consideration of dishes containing specific ingredients from the user's past meal history. The consideration unit can also provide consideration results by excluding dishes that the user has avoided in the past. For example, the consideration unit provides consideration results by excluding avoided dishes based on the user's past meal history. In this way, the optimal consideration method can be selected by analyzing the user's past meal history. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's past meal history data into the generation AI and cause the generation AI to select the optimal consideration method.

[0086] The consideration unit can take into account the user's current health condition and lifestyle habits. For example, when the user inputs the results of a health checkup, the consideration unit suggests optimal dishes based on the information. For example, the consideration unit suggests health-conscious dishes based on the user's health checkup results. The consideration unit can also suggest appropriate dishes by taking into account the user's lifestyle habits (such as exercise amount and sleep time). For example, the consideration unit can suggest optimal dishes based on the user's lifestyle habit data. Furthermore, the consideration unit can also suggest optimal dishes by taking into account the user's current physical condition (such as fatigue level and stress level). For example, the consideration unit can suggest optimal dishes based on the user's physical condition data. This enables more appropriate suggestions to be made by taking into account the user's health condition and lifestyle habits. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's health condition and lifestyle habit data into the generation AI and cause the generation AI to perform the consideration.

[0087] The consideration unit can make suggestions taking into account the user's dietary restrictions and religious restrictions. For example, if the user is a vegetarian, the consideration unit preferentially suggests dishes that do not contain meat. For example, the consideration unit suggests meat-free dishes based on the fact that the user is a vegetarian. Furthermore, if the user has specific religious restrictions (such as halal or kosher), the consideration unit can also suggest dishes that comply with those restrictions. For example, the consideration unit suggests corresponding dishes based on the user's religious restrictions. Furthermore, if the user has specific dietary restrictions (such as low carbohydrate or low fat), the consideration unit can also suggest dishes that comply with those restrictions. For example, the consideration unit suggests corresponding dishes based on the user's dietary restrictions. This enables appropriate suggestions to be made by taking the user's dietary restrictions and religious restrictions into consideration. Some or all of the above-described processing by the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's dietary restriction and religious restriction data into the generation AI and have the generation AI execute the suggestions.

[0088] The consideration unit can estimate the user's emotions and determine the priority of information to be considered based on the estimated user emotions. The consideration unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the consideration unit analyzes facial expression data of the user captured by a camera to estimate the emotions. The consideration unit can also estimate the user's emotions using voice analysis technology. For example, the consideration unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the consideration unit determines the priority of information to be considered based on the estimated user emotions. For example, if the user is nervous, simple and important information can be provided preferentially. If the user is relaxed, detailed information can be provided preferentially. Furthermore, if the user is in a hurry, information that can be provided quickly can be provided preferentially. This enables more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit may input user emotion data to the generation AI and cause the generation AI to prioritize information based on the emotion.

[0089] The consideration unit can suggest regional ingredients and dishes by taking into account the user's geographical location information. The consideration unit, for example, prioritizes suggestions of local specialties and famous dishes of the region where the user is currently located. For example, the consideration unit suggests menus of nearby restaurants based on the user's location information. The consideration unit can also suggest dishes containing regional ingredients based on the user's location information. For example, the consideration unit suggests dishes containing regional ingredients based on the user's location information. In this way, regional ingredients and dishes can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing by the consideration unit may be performed using AI, for example, or may be performed without using AI. For example, the consideration unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the suggestions.

[0090] The consideration unit can analyze the user's social media activity and take into account related preferences and allergy information. The consideration unit, for example, considers the menu of a restaurant where the user has checked in on social media. For example, the consideration unit analyzes the content of the user's social media posts and takes into account related preferences and allergy information. The consideration unit can also take into account related preferences and allergy information with reference to the activities of the user's friends on social media. For example, the consideration unit takes into account related preferences and allergy information based on the activities of the user's friends on social media. In this way, related preferences and allergy information can be taken into account by analyzing the user's social media activity. Some or all of the above-described processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the user's social media activity data into the generation AI and cause the generation AI to perform the consideration.

[0091] The consideration unit can customize the consideration method by reflecting the user's past feedback. The consideration unit adjusts the consideration method, for example, based on feedback provided by the user in the past. For example, the consideration unit preferentially displays specific consideration results from the user's past feedback. The consideration unit can also improve the accuracy of the consideration results based on the user's feedback. For example, the consideration unit improves the accuracy of the consideration results based on the user's feedback. In this way, the consideration method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the consideration unit may be performed using AI, for example, or may be performed without using AI. For example, the consideration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the consideration method.

[0092] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. The translation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the translation unit can analyze the user's facial expression data captured by a camera to estimate emotions. The translation unit can also estimate the user's emotions using voice analysis technology. For example, the translation unit can analyze the user's tone and speed of voice to estimate emotions. The translation unit can then adjust the translation expression based on the estimated user emotions. For example, if the user is nervous, simple and easy-to-understand expressions can be used. If the user is relaxed, detailed and rich expressions can be used. If the user is in a hurry, concise and quick expressions can be used. This allows the translation expression to be adjusted according to the user's emotions, resulting in a more appropriate translation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit may input user emotion data into the generation AI and have the generation AI adjust the translation expression method based on the emotion.

[0093] The translation unit can provide information including the cultural background and history of a dish during translation. For example, the translation unit can provide the origin and historical background of a dish by including it in the translation results. For example, the translation unit can translate information about the origin and historical background of a dish and provide it to the user. The translation unit can also provide the cultural meaning and symbolism of a dish by including it in the translation results. For example, the translation unit can translate information about the cultural meaning and symbolism of a dish and provide it to the user. Furthermore, the translation unit can provide the translation results by including information about how a dish developed in a particular region or era. For example, the translation unit can translate information about the development of a dish and provide it to the user. This allows the user to have a deeper understanding by including the cultural background and history of the dish. Some or all of the above-described processing in the translation unit can be performed using, or without, AI. For example, the translation unit can input information about the cultural background and history of a dish into a generation AI and have the generation AI perform the translation.

[0094] During translation, the translation unit can provide information including details of the cooking method and ingredients of the dish. For example, the translation unit translates the cooking steps of the dish in detail and provides it to the user. For example, the translation unit translates information regarding the cooking steps of the dish and provides it to the user. The translation unit can also provide the main ingredients used in the dish and their roles by including them in the translation result. For example, the translation unit translates information regarding the ingredients of the dish and their roles and provides it to the user. Furthermore, the translation unit can also provide tips and precautions regarding the cooking method of the dish by including them in the translation result. For example, the translation unit translates tips and precautions regarding the cooking method of the dish and provides it to the user. In this way, by including details of the cooking method and ingredients of the dish, more specific information can be provided to the user. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input information regarding the cooking method and details of the ingredients of the dish into the generation AI and have the generation AI perform the translation.

[0095] During translation, the translation unit can improve the accuracy of the translation by referring to the user's past translation history. For example, the translation unit may preferentially use similar expressions based on content that the user has translated in the past. For example, the translation unit may preferentially use specific terms and expressions from the user's past translation history. The translation unit can also analyze the user's past translation history and suggest the most appropriate expression. For example, the translation unit may suggest the optimal expression based on the user's past translation history. In this way, by referring to the user's past translation history, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit may input the user's past translation history data into the generation AI and have the generation AI improve the accuracy of the translation.

[0096] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. The translation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the translation unit can analyze the user's facial expression data captured by a camera to estimate emotions. The translation unit can also estimate the user's emotions using voice analysis technology. For example, the translation unit can analyze the user's tone and speed of voice to estimate emotions. The translation unit can also adjust the length of the translation based on the estimated user emotions. For example, if the user is in a hurry, the translation unit can provide a short, concise translation. If the user is relaxed, the translation unit can provide a longer translation with detailed explanations. If the user is excited, the translation unit can provide a translation with visually stimulating effects. This allows the translation length to be adjusted according to the user's emotions, resulting in a more appropriate translation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit may input user emotion data into the generation AI and have the generation AI adjust the length of the translation based on the emotion.

[0097] During translation, the translation unit can determine the priority of translation based on the time when the dishes were submitted. For example, the translation unit prioritizes translation of seasonal menus or dishes available for a limited time. For example, the translation unit determines the priority of translation based on seasonal menus or dishes available for a limited time. Furthermore, if a user is participating in a specific event or festival, the translation unit can prioritize translation of dishes related to the event. For example, the translation unit determines the priority of translation based on dishes related to the event or festival. Furthermore, the translation unit can prioritize translation of menus of restaurants that the user visits during a specific time period. For example, the translation unit determines the priority of translation based on menus served during a specific time period. In this way, determining the priority of translation based on the time when the dishes were submitted enables more appropriate information to be provided. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input data regarding the time when the dishes were submitted to the generation AI and have the generation AI determine the priority of translation.

[0098] During translation, the translation unit can adjust the order of translation based on the relevance of dishes. For example, the translation unit prioritizes translating the main dish, followed by translating the side dishes and desserts. For example, the translation unit adjusts the order of translation based on the main dish. Furthermore, if a user selects a specific dish, the translation unit can prioritize translating other dishes related to that dish. For example, the translation unit adjusts the order of translation based on other dishes related to the specific dish. Furthermore, if a user is interested in a specific ingredient, the translation unit can prioritize translating dishes containing that ingredient. For example, the translation unit adjusts the order of translation based on dishes containing the specific ingredient. In this way, adjusting the order of translation based on the relevance of dishes enables more appropriate information to be provided. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input data regarding the relevance of dishes to a generation AI and have the generation AI adjust the order of translation.

[0099] During translation, the translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise. For example, if the user is a cooking expert, the translation unit provides a translation that uses a lot of technical terminology. For example, the translation unit provides a translation that uses a lot of technical terminology for cooking experts. Furthermore, if the user is not familiar with cooking, the translation unit can also provide a simple, easy-to-understand translation. For example, the translation unit provides a simple, easy-to-understand translation for users who are not familiar with cooking. Furthermore, the translation unit can adjust the use of appropriate technical terminology based on the user's past translation history. For example, the translation unit adjusts the use of appropriate technical terminology based on the user's past translation history. This allows for a more appropriate translation to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data regarding the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0100] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the providing unit analyzes facial expression data of the user captured by a camera to estimate the emotions. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit adjusts the information provision method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate information provision by adjusting the information provision method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the method of providing information based on the emotion.

[0101] When providing information, the providing unit can select the optimal providing method by referring to the user's past operation history. The providing unit selects the optimal providing method, for example, based on a display method previously selected by the user. For example, the providing unit prioritizes providing specific information based on the user's past operation history. The providing unit can also analyze the user's past operation history and suggest the most appropriate providing method. For example, the providing unit suggests the optimal providing method based on the user's past operation history. In this way, the optimal providing method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal providing method.

[0102] The providing unit can customize the provided content according to the user's current task when providing information. For example, if the user is eating, the providing unit can prioritize providing information related to meals. For example, the providing unit can provide information related to meals based on the fact that the user is eating. Furthermore, if the user is sightseeing, the providing unit can prioritize providing information related to tourist spots. For example, the providing unit can provide information related to tourist spots based on the fact that the user is sightseeing. Furthermore, if the user is shopping, the providing unit can prioritize providing information related to shopping. For example, the providing unit can provide information related to shopping based on the fact that the user is shopping. This enables more appropriate information to be provided by customizing the provided content according to the user's current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data related to the user's current task to a generation AI and cause the generation AI to customize the provided content.

[0103] The providing unit can improve the providing method by reflecting user feedback when providing information. For example, if a user provides feedback on the provided information, the providing unit improves the providing method based on that feedback. For example, the providing unit preferentially provides specific information based on the user feedback. The providing unit can also improve the accuracy of the providing method based on the user feedback. For example, the providing unit improves the accuracy of the providing method based on the user feedback. In this way, the providing method can be improved by reflecting the user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0104] The providing unit can estimate the user's emotion and adjust the information display method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the providing unit analyzes the user's facial expression data captured by a camera to estimate the emotion. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the providing unit adjusts the information display method based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate information provision by adjusting the information display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the information display method based on the emotion.

[0105] When providing information, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. For example, the providing unit provides a display method optimized for the smartwatch screen size. This makes it possible to select the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0106] When providing information, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the display content based on the language setting of the user's device. For example, the providing unit automatically sets the display content based on the language setting of the device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function based on the user's use of multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide information in that language. For example, the providing unit provides information based on the language selected by the user. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to perform multilingual support for the display content.

[0107] When providing information, the providing unit can analyze the user's social media activity and provide related information. The providing unit, for example, provides information about places where the user has checked in on social media. For example, the providing unit can provide related information based on the user's check-in information. The providing unit can also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide related information based on the user's posts. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit can provide related information based on the activities of the user's friends. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === For example, the analysis unit can detect the user's facial expression and tone of voice using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A can infer their emotions. The processing unit is realized by the specific processing unit 290 of the data processing device 12 and selects appropriate food based on the user's preferences and allergy information. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates information about food and manners into the user's native language. The provision unit is realized by the control unit 46A of the smart device 14 and provides the translated information to the user as a text display or voice guidance. === Hard Collateral 1-2 === For example, the analysis unit can detect the user's facial expression and tone of voice using the camera 42 and microphone 238 of the smart glasses 214, and estimate the user's emotions using the control unit 46A. The processing unit is realized by the specific processing unit 290 of the data processing device 12 and selects appropriate food based on the user's preferences and allergy information. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates information about food and manners into the user's native language. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the translated information to the user as a text display or voice guidance. === Hard Collateral 1-3 === For example, the analysis unit can detect the user's facial expression and tone of voice using the camera 42 and microphone 238 of the headset terminal 314, and estimate the emotion using the control unit 46A. The processing unit is realized by the specific processing unit 290 of the data processing device 12, and selects appropriate food based on the user's preferences and allergy information. The translation unit is realized by the specific processing unit 290 of the data processing device 12, and translates information about food and manners into the user's native language. The provision unit is realized by the control unit 46A of the headset terminal 314, and provides the translated information to the user as a text display or voice guidance. === Hard Collateral 1-4 === For example, the analysis unit can detect the user's facial expressions and tone of voice using the camera 42 and microphone 238 of the robot 414, and estimate the user's emotions using the control unit 46A. The processing unit is realized by the specific processing unit 290 of the data processing device 12, and selects appropriate food based on the user's preferences and allergy information. The translation unit is realized by the specific processing unit 290 of the data processing device 12, and translates information about food and manners into the user's native language. The provision unit is realized by the control unit 46A of the robot 414, and provides the translated information to the user as a text display or voice guidance.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The navigation system also collects real-time feedback from the user while they are eating, and the analysis unit can adjust the analysis results based on that feedback. For example, if a user provides feedback such as "delicious" or "spicy" after eating a dish, the analysis unit can adjust the next analysis results based on that feedback. Also, if a user provides positive feedback about a particular dish, the analysis unit can prioritize the analysis of other dishes related to that dish. Furthermore, if a user provides negative feedback, the analysis unit can also exclude information related to that dish. This allows the system to provide more personalized analysis results by reflecting the user's real-time feedback.

[0110] The navigation system can further estimate the user's emotions and adjust the information provision method in the provision unit based on the estimated emotions. For example, if the user is excited, the provision unit can provide information with a visually stimulating effect. If the user is relaxed, the provision unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the provision unit can provide concise information that focuses on the main points. In this way, by adjusting the information provision method according to the user's emotions, more appropriate information can be provided.

[0111] The navigation system can also monitor the user's health condition, and the analysis unit can adjust the analysis results based on that information. For example, if the user inputs the results of a health check, the analysis unit can prioritize analysis of healthy dishes based on that information. Also, if the user sets a specific health goal (e.g., weight loss or muscle building), the analysis unit can prioritize analysis of dishes that match that goal. Furthermore, if the user has a specific health condition (e.g., high blood pressure or diabetes), the analysis unit can also prioritize analysis of dishes that correspond to that condition. This allows the system to provide more appropriate analysis results by taking the user's health condition into consideration.

[0112] The navigation system can further estimate the user's emotions and adjust the translation expression in the translation unit based on the estimated emotions. For example, if the user is nervous, the translation unit can use simple and easy-to-understand expressions. If the user is relaxed, the translation unit can use detailed and rich expressions. Furthermore, if the user is in a hurry, the translation unit can use concise and quick expressions. This allows the translation expression to be adjusted according to the user's emotions, thereby providing a more appropriate translation.

[0113] The navigation system can further analyze the user's social media activity and use that information in the analysis unit to adjust the analysis results. For example, the system can analyze the menus of restaurants that the user has checked in to on social media. It can also analyze the content of the user's social media posts to provide related food information. It can also provide related food information by taking into account the activities of the user's friends on social media. This allows the system to provide more appropriate analysis results by analyzing the user's social media activity.

[0114] The navigation system can further estimate the user's emotions and adjust the suggestion method in the consideration unit based on the estimated emotions. For example, if the user is nervous, the consideration unit can provide simple options and display detailed information later. Alternatively, if the user is relaxed, the consideration unit can provide detailed options and suggest customizable information. Furthermore, if the user is in a hurry, the most important information can be displayed first and the consideration results can be provided quickly. This allows for more appropriate suggestions to be made by adjusting the suggestion method according to the user's emotions.

[0115] The navigation system can also improve the information provision method in the provision unit by reflecting the user's past feedback. For example, if the user provides feedback on the provided information, the provision method can be improved based on that feedback. It can also provide specific information preferentially based on the user's feedback. Furthermore, the accuracy of the provision method can be improved based on the user's feedback. In this way, the provision method can be improved by reflecting the user's feedback.

[0116] The navigation system can further estimate the user's emotions and adjust the analysis method in the analysis unit based on the estimated emotions. For example, if the user is nervous, a simple analysis result can be provided, with detailed information displayed later. Alternatively, if the user is relaxed, a detailed analysis result can be provided, including background information on the dishes. Furthermore, if the user is in a hurry, the most important information can be displayed first, and analysis results can be provided quickly. This allows the analysis method to be adjusted according to the user's emotions, making it possible to provide more appropriate analysis results.

[0117] The navigation system can further adjust the analysis results of the analysis unit by taking into account the user's geographical location information. For example, it can prioritize analysis of local specialties and famous dishes from the area where the user is currently located. It can also analyze menus of nearby restaurants based on the user's location information. It can also analyze dishes that contain ingredients unique to the area based on the user's location information. In this way, it can provide information specific to the area by taking into account the user's geographical location information.

[0118] The navigation system can further estimate the user's emotions and adjust the way information is displayed by the information providing unit based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide more appropriate information by adjusting the way information is displayed according to the user's emotions.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The analyzer analyzes the menu or image. For example, the analyzer may use image recognition technology to analyze the image of the menu and convert it into text data. The analyzer may also use text analysis technology to analyze the text of the menu and identify the type of food. For example, the analyzer may use OCR technology to read a printed menu and convert it into text data. Step 2: The processing unit processes the user's preferences and allergy information based on the information analyzed by the analysis unit. For example, the processing unit estimates the user's preferences based on the user's past selection history and selects appropriate foods taking into account the allergy information. The processing unit can also suggest foods that are suitable for allergies based on the user's allergy information. For example, the processing unit selects foods that do not contain allergens based on the allergy information entered by the user. Step 3: The translation unit translates the information processed by the processing unit. For example, the translation unit uses machine translation technology to translate information about food into the user's native language. The translation unit can also translate unique manners related to food. For example, the translation unit translates the manners and etiquette for eating at Japanese restaurants and provides them to the user. Step 4: The providing unit provides the information translated by the translation unit to the user. The providing unit provides the information using, for example, a text display or audio guidance. The providing unit can also select the optimal display method depending on the user's device. For example, the providing unit displays the information in the optimal format depending on the device, such as a smartphone or tablet.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: an analysis unit that analyzes menus or images; a processing unit that processes information analyzed by the analysis unit based on user preferences and allergy information; a translation unit that translates the information processed by the processing unit; and a provision unit that provides the user with the information translated by the translation unit.

2. The system of claim 1 , wherein the analyzer analyzes a menu or image to identify a particular food item.

3. The system of claim 1 , wherein the processing unit processes based on user preference and allergy information.

4. The system of claim 1 , wherein the translation unit translates information about a specified food and unique manners associated with the food.

5. The system according to claim 1 , wherein the providing unit presents translated information to the user.

6. The consideration unit Suggesting foods that are suitable for allergies based on the user's allergy information 4. The system of claim 3.

7. The translation unit Translating etiquette in Japanese restaurants and dining etiquette 5. The system of claim 4.

8. The analysis unit Estimate user emotions and adjust menu and image analysis methods based on the estimated user emotions 2. The system of claim 1.

9. The analysis unit When analyzing menus and images, improve the accuracy of the analysis by referring to the user's past selection history.

2. The system of claim 1.

Images